Assessment and Treatment of Pain during In‐Office Otolaryngology Procedures: A Systematic Review
Bibliographic record
Abstract
Objective To qualitatively assess practices of periprocedural pain assessment and control and to evaluate the effectiveness of interventions for pain during in‐office procedures reported in the otolaryngology literature through a systematic review. Data Sources PubMed, CINAHL, and Web of Science searches from inception to 2018. Review Methods English‐language studies reporting qualitative or quantitative data for periprocedural pain assessment in adult patients undergoing in‐office otolaryngology procedures were included. Risk of bias was assessed via the Cochrane Risk of Bias or Cochrane Risk of Bias in Non‐Randomized Studies of Interventions tools as appropriate. Two reviewers screened all articles. Bias was assessed by 3 reviewers. Results Eighty‐six studies describing 32 types of procedures met inclusion criteria. Study quality and risk of bias ranged from good to serious but did not affect assessed outcomes. Validated methods of pain assessment were used by only 45% of studies. The most commonly used pain assessment was patient tolerance, or ability to simply complete a procedure. Only 5.8% of studies elicited patients’ baseline pain levels prior to procedures, and a qualitative assessment of pain was done in merely 3.5%. Eleven unique pain control regimens were described in the literature, with 8% of studies failing to report method of pain control. Conclusion Many reports of measures and management of pain for in‐office procedures exist but few employ validated measures, few are standardized, and current data do not support any specific pain control measures over others. Significant opportunity remains to investigate methods for improving patient pain and tolerance of in‐office procedures.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".